Decision Trees on the Foreign Exchange Market

  • Juszczuk PrzemyslawEmail author
  • Kozak Jan
  • Trynda Katarzyna
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 57)


In this article we present a novel approach to generate a data set directly from real-world forex market data. The data are transformed into a decision table. Every single object in such a table consists of conditional attributes—in this case values of technical analysis indicators as well as of the decision class (BUY, SELL or WAIT). Our second goal was to test the quality of the classification based on two well-known algorithms used for decision tree construction: the CART algorithm and the C4.5 algorithm. All experiments were conducted on three different currency pairs—with 3 data sets for each pair.


Forex market Decision tree CART C4.5 algorithm 


  1. 1.
    Bahrepoura, M., Akbarzadeh-T, M.-R., Yaghoobia, M., Naghibi-S, M.-B.: An adaptive ordered fuzzy time series with application to FOREX. Exp. Syst. Appl. 38(1), 475–485 (2011)CrossRefGoogle Scholar
  2. 2.
    Boryczka, U., Kozak, J.: Enhancing the effectiveness of ant colony decision tree algorithms by co-learning. Appl. Soff Comput. 30, 166–178 (2015)CrossRefGoogle Scholar
  3. 3.
    Brabazon, T., ONeill, M.: Trading foreign exchange markets using evolutionary automatic programming. In: GECCO 2002: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference (2002)Google Scholar
  4. 4.
    Caginalp, G., Laurent, H.: The predictive power of price patterns. Appl. Math. Financ. 5(3–4), 181–205 (1998)CrossRefGoogle Scholar
  5. 5.
    Cassetti, M.D.: A neural network system for reliable trading signals. Tech. Anal. Stock. Commod. 11(6), 78–84 (1993)Google Scholar
  6. 6.
    Cheol-Ho, P., Irwin, S.H.: What do we know about the profitability of technical analysis? J. Econ. Surv. 21(4), 786–826 (2007)Google Scholar
  7. 7.
    Czekalski, P., Niezabitowski, M., Styblinski, R.: ANN for FOREX forecasting and trading. Control Systems and Computer Science (CSCS 2015), pp. 322–328 (2015)Google Scholar
  8. 8.
    Froelich, W., Juszczuk, P.: Predictive capabilities of adaptive and evolutionary fuzzy cognitive Maps—a comparative study. Studies in Computational Intelligence, vol. 252, pp. 153–174. Springer (2009)Google Scholar
  9. 9.
    Gunasekarage, A., Power, D.M.: The profitability of moving average trading rules in South Asian stock markets. Emerg. Markets Rev. 2, 17–33 (2001)CrossRefGoogle Scholar
  10. 10.
    Kim, K.-J.: Financial time series forecasting using support vector machines. Neurocomputing 55(1–2), 307–319 (2003)CrossRefGoogle Scholar
  11. 11.
    Kirkpatrick II, C.D., Dahlquist Julie, R., Technical Analysis. Complete Resource for Financial Market Technicians. FT Press (2010)Google Scholar
  12. 12.
    Korczak, J., Roger, P.: Stock timing using genetic algorithms. Appl. Stoch. Model. Bus. Ind. 18(2), 121–134 (2002)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Kozak, J., Boryczka, U.: Multiple boosting in the ant colony decision forest meta-classifier. Knowl. Based Syst. 75, 141–151 (2015)CrossRefGoogle Scholar
  14. 14.
    Lai, K.K., Yu, L., Wang, S.: A neural network and web-based decision support system for forex forecasting and trading. Data Min. Knowl. Manag. 3327, 243–253 (2005)CrossRefGoogle Scholar
  15. 15.
    Lim, T.-S., Loh, W.-Y., Shih, Y.-S.: A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Mach. Learn. 40(3), 203–228 (2000)Google Scholar
  16. 16.
    Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)Google Scholar
  17. 17.
    Rhea, R.: Dow Theory. Barron’s, New York (1932)Google Scholar
  18. 18.
    Safavin, S.R., Landgrebe, D.: A survey of decision tree classifier methodology. IEEE Trans. Syst. 21(3), 660–674 (1991)Google Scholar
  19. 19.
    Taylor, M.P., Allen, H.: Extended evidence on the usage of technical analysis in foreign exchange. Int. J. Finance Econ. 11, 327–338 (2006)Google Scholar
  20. 20.
    Taylor, M.P., Allen, H.: The use of technical analysis in the foreign exchange market. J. Int. Money Finance 11, 304–314 (1992)CrossRefGoogle Scholar
  21. 21.
    Tkacz, M.: Artificial neural networks in incomplete data sets processing. In: IntelligentInformation Processing and Web Mining, pp. 577–583 (2005)Google Scholar
  22. 22.
    Wasendorf Sr., R.R.: Foreign Currency Trading: From the Fundamentals to the Fine Points. McGraw-Hill (1997)Google Scholar

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Authors and Affiliations

  • Juszczuk Przemyslaw
    • 1
    Email author
  • Kozak Jan
    • 2
  • Trynda Katarzyna
    • 1
  1. 1.Institute of Computer Science, University of SilesiaSosnowiecPoland
  2. 2.Chair of Knowledge Engineering, Faculty of Informatics and CommunicationUniversity of EconomicsKatowicePoland

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